An object-based video coding framework for video sequences obtained from static cameras

This paper presents a novel object-based video coding framework for videos obtained from a static camera. As opposed to most existing methods, the proposed method does not require explicit 2D or 3D models of objects and hence is general enough to cater for varying types of objects in the scene. The proposed system detects and tracks objects in the scene and learns the appearance model of each object online using incremental principal component analysis (IPCA). Each object is then coded using the coefficients of the most significant principal components of its learned appearance space. Due to smooth transitions between limited number of poses of an object, usually a limited number of significant principal components contribute to most of the variance in the object's appearance space and therefore only a small number of coefficients are required to code the object. The rigid component of the object's motion is coded in terms of its affine parameters. The framework is applied to compressing videos in surveillance and video phone domains. The proposed method is evaluated on videos containing a variety of scenarios such as multiple objects undergoing occlusion, splitting, merging, entering and exiting, as well as a changing background. Results on standard MPEG-7 videos are also presented. For all the videos, the proposed method displays higher Peak Signal to Noise Ratio (PSNR) compared to MPEG-2 and MPEG-4 methods, and provides comparable or better compression.